Statistical methods for understanding multilevel and longitudinal data with application to health studies in South Wales

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Many epidemiological studies produce longitudinal data, where there are repeated measurements on the same individuals or groups. Unfortunately, these measurements may not be available for all individuals at all the measurement times, a problem known generically as missingness. The most common type of missingness is dropout, when individuals miss a measurement and then never reappear.If people drop out because they are becoming increasingly unhealthy, then over time the surviving individuals are become less representative, since only the healthier people remain. Though this can introduce bias into conclusions drawn from the dataset, many scientists still ignore the problem entirely because the methods available for adjusting for dropout tend to be complicated and time-consuming. In this fellowship I will develop my recently proposed technique for dealing with dropout, and apply it to a 25-year study of cardiovascular disease in Wales.Additionally, similar methods have the potential to be applied to another structure common in epidemiology, in which people cluster within neighbourhoods. While these techniques will be applicable in a wide variety of situations, this fellowship will focus in particular on two health studies from South Wales, and will investigate the dynamics and geography of physical and mental health in this region.